code: def export() -> None:
      # input size: (2, 3, 4, 5)
      x = np.random.randn(2, 3, 4, 5).astype(np.float32)
      s = np.random.randn(3).astype(np.float32)
      bias = np.random.randn(3).astype(np.float32)
      mean = np.random.randn(3).astype(np.float32)
      var = np.random.rand(3).astype(np.float32)
      y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
  
      node = onnx.helper.make_node(
          "BatchNormalization",
          inputs=["x", "s", "bias", "mean", "var"],
          outputs=["y"],
      )
  
      # output size: (2, 3, 4, 5)
      expect(
          node,
          inputs=[x, s, bias, mean, var],
          outputs=[y],
          name="test_batchnorm_example",
      )
  
      # input size: (2, 3, 4, 5)
      x = np.random.randn(2, 3, 4, 5).astype(np.float32)
      s = np.random.randn(3).astype(np.float32)
      bias = np.random.randn(3).astype(np.float32)
      mean = np.random.randn(3).astype(np.float32)
      var = np.random.rand(3).astype(np.float32)
      epsilon = 1e-2
      y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(
          np.float32
      )
  
      node = onnx.helper.make_node(
          "BatchNormalization",
          inputs=["x", "s", "bias", "mean", "var"],
          outputs=["y"],
          epsilon=epsilon,
      )
  
      # output size: (2, 3, 4, 5)
      expect(
          node,
          inputs=[x, s, bias, mean, var],
          outputs=[y],
          name="test_batchnorm_epsilon",
      )
  
file_path: onnx/onnx/backend/test/case/node/batchnorm.py
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 1 for calling the BatchNormalization obj's underlying member method code with example usage: obj.export() without return types